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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
11th October 2026 Languages English English English The Department of Structural Engineering has a vacancy for a PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High
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modelling under uncertainty. Experience with quantitative research methods, stated choice experiments, simulation modelling, agent-based modelling, transport modelling, econometrics, or related approaches is
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-reviewed academic works. Good oral and written presentation skills in English. You must have prior experience with the MPI and OpenMP programming models, and provide an example of a CFD simulation program
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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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to new developments. The candidate will join the System Software Group , and study the performance and scalability characteristics of adapting scientific simulation software to current and future
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to investigate cascading cyber effects, cyber resilience, and advanced cyber range modelling. The research combines simulation, software development, and experimental validation in the Norwegian Cyber Range
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the position We are seeking a highly motivated PhD candidate to investigate cascading cyber effects, cyber resilience, and advanced cyber range modelling. The research combines simulation, software
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time